Prognostic Analysis of Diffuse Large B-Cell Lymphoma Patients Based on Clinical Characteristics, TP53 Mutation Status, and Number of Co-mutated Genes

Prognostic Analysis of Diffuse Large B-Cell Lymphoma Patients Based on Clinical Characteristics, TP53 Mutation Status, and Number of Co-mutated Genes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Analysis of Diffuse Large B-Cell Lymphoma Patients Based on Clinical Characteristics, TP53 Mutation Status, and Number of Co-mutated Genes Xianyi Wu, Jie Zhu, Taohua Deng, Meilian Qin, Wenyuan Lin, Fujun Qu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6665588/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We retrospectively analyzed 59 patients newly diagnosed with diffuse large B-cell lymphoma (DLBCL) in our hospital, based on next-generation sequencing mutation analysis and clinical characteristics. One or more mutations were detected in all patients, with a median mutation count of four (range 1–9). The genes with the highest mutation frequencies (> 10%) included TP53 (37.3%), KMT2D (27.1%), CD79B (25.4%), PIM1 (22.0%), TNFAIP3 (18.6%), MYD88 (16.9%), IRF4 (15.3%), B2M (13.6%), TNFRSF14 (11.9%), CREBBP (10.2%), and SOCS1 (10.2%). Statistical analysis revealed that B symptoms, an International Prognostic Index score > 2, and poor treatment efficacy were associated with inferior progression-free survival (PFS) and overall survival (OS). A mutation count greater than four, TP53 mutation, and KMT2D mutation combined with more than four mutations led to poorer OS and PFS, while IRF4 mutation was associated with better OS and a trend towards improved PFS. Therefore, it might be possible to identify high-risk patients in DLBCL through clinical characteristics and genetic mutation profiling, which may allow for personalized treatment leading to improved prognosis. Diffuse large B-cell lymphoma clinical features next generation sequencing TP53 IRF4 KMT2D Figures Figure 1 Figure 2 Figure 3 1. BACKGROUND Diffuse large B-cell lymphoma (DLBCL) stands as the most prevalent subtype of non-Hodgkin lymphoma (NHL) in adults, responsible for approximately 35–40% of NHL cases ( 26 ). It is a disease characterized by substantial heterogeneity, with marked variations in biological, pathological, clinical, and genomic characteristics among patients ( 6 ). The development of DLBCL is highly complex, and involves altered oxidative phosphorylation and differential expression of genes involved in B-cell receptor (BCR) signaling and host inflammatory responses ( 17 ). The pathways affected play a critical role in the development and maintenance of DLBCL, as well as in the response to treatment. At present, the R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) regimen is the standard first-line treatment for DLBCL, with a cure rate higher than 60% across all patients (23; 24). However, up to 40% of patients exhibit refractory disease, and they are prone to relapse after remission. The prognosis for this group is generally poor, and most patients ultimately succumb to lymphoma. Consequently, these patients have become the primary focus of current research ( 5 ). In recent years, the application of next-generation sequencing (NGS) technology has led to extensive genomic research on DLBCL. This research progress has aided the genetic classification of DLBCL subtypes, which enhanced therapeutic efficacy by expanding treatment approaches to include small-molecule targeted drugs in addition to the R-CHOP regimen (18; 32). Since there is relatively little research on the impact of co-mutated genes on treatment response and prognosis of DLBCL patients, this study analyzed potential alterations in 114 genes related to B-cell lymphoma in 59 newly diagnosed DLBCL patients. The findings presented herein reveal new insights into the association between co-mutated genes and clinical characteristics, and their impact on treatment response and prognosis. 2. PATIENTS AND METHODS 2.1 Data collection The current study involved 59 patients with DLBCL who were treated at the Hematology Clinic of the Affiliated Hospital of Guilin Medical University from January 2022 through August 2024. All patients provided written informed consent for the use of their data in scientific research. Each patient was diagnosed with DLBCL in accordance with the WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues, 5th Edition, utilizing histopathological biopsy and immunohistochemical staining methods. The immunohistochemical assessment included a panel of markers such as CD20, CD19, CD79b, CD3, CD10, BCL2, BCL6, Ki-67, CD5, CD30, CD21, CD23, MUM1, EBER, and TP53. All histological slides were reviewed and confirmed by two experienced pathologists. The criteria for inclusion encompassed individuals aged between 18 and 80 years, irrespective of gender, who were all newly diagnosed cases. Criteria for exclusion comprised patients lacking comprehensive clinical data or those who were lost to follow-up; patients who did not undergo treatment; patients who had received radiotherapy or chemotherapy prior to enrollment; and patients with other hematological malignancies or malignant cachexia. Post-diagnosis, all patients were administered the standard first-line treatment and their responses to treatment were evaluated using PET-CT or CT scans. Patients who did not achieve a partial response (PR) were scheduled for second-line therapy or enrollment in clinical trials. The exclusion criteria for the study population included patients with incomplete clinical records or those who were lost to follow-up, individuals who had previously undergone radiotherapy or chemotherapy, and those with additional hematological malignancies or consumptive malignant diseases. Baseline clinical data, which comprised information on the patients’ gender, age, lactate dehydrogenase (LDH) levels, Ann Arbor Stage, Eastern Cooperative Oncology Group performance status (ECOG PS), bone marrow biopsy results, B symptoms, treatment modalities, and therapeutic efficacy (encompassing PR, complete response [CR], stable disease [SD], and progressive disease [PD]), Lugano 2014( 4 )), were collected at the time of initial diagnosis. Progression-free survival (PFS) was defined as the duration from diagnosis until the first evidence of disease progression or death from any cause, while overall survival (OS) was defined as the time from diagnosis to the last follow-up visit or death from any cause. Informed consent was obtained from all patients, and the study was conducted under the approval of the Medical Ethics Committee of the Affiliated Hospital of Guilin Medical University, adhering strictly to the tenets of the Declaration of Helsinki. 2.2 NGS sequencing Patient diagnosis was conducted at the Hematology laboratory of the Affiliated Hospital of Guilin Medical University by NGS sequencing of genomic DNA isolated from formalin-fixed and paraffin-embedded tumor sections. The gene panel was designed to include genes frequently altered in B-cell lymphomas. Genomic DNA was extracted from formalin-fixed paraffin-embedded tissues (FFPE) samples using a QIAamp Blood DNA Mini Kit (Cat No. 51104, QIAGEN, Hilden, Germany) and 100 ng of genomic DNA was further used to prepare a captured library using Enzyme Plus Library Prep kit (Cat. No C11111, iGeneTech, Shanghai, China) and TargetSeq One Hyb & Wash Kit (Module C, for Illumina) (Cat. No C10331, iGeneTech, Shanghai, China). Targeted sequencing of B-NHL related genes was performed at Shanghai Rightongene Biotechnology Co, Ltd. (Shanghai, China) on the Hiseq2000 (Illumina, USA) sequencing platform with 2 × 150 bp pair-end protocol (HiSeq Cluster Kit v4, Illumina, USA). The final library was diluted to a final DNA concentration of 2 nM, which was detected using Qubit™ dsDNA Quantification Assay Kits (Q32851, Invitrogen, Thermofisher, USA). Denature and dilute the final libraries to a final DNA concentration of 20 pM for cluster generation. The original sequencing was aligned with the human reference genome GRCh37. Single nucleotide variations (SNVs) and insertion and deletion (Indels) were screened by Shanghai Rightongene Biotechnology Co., Ltd. (Shanghai, China) based on the filtering conditions: ( 1 ) reads base quality < 20; ( 2 ) variants on the positive-strand and negative-strand were inconsistent; ( 3 ) variant allele frequency (VAF) < 1% and individual mutant reads < 20; ( 4 ) dbSNP (v147) sites that were not existed in COSMIC database; ( 5 ) SNPs (single nucleotide polymorphisms) or Indels (insertions and deletions) with a mutation allele frequency (MAF) ≥ 0.001 in databases of 1,000 genomes project, 1,000 genome East Asian, ExAC all, or ExAC East Asian were removed. ( 6 ) missense mutations were predicted not deleterious by Bioinformatics Tools (sift > 0.05, Polyphen2_HVAR_pred < 0.447, and CADD_phred < 15); ( 7 ) mutations were predicted to be synonymous. DNA sequencing was conducted on all patients enrolled in the study, with detection, annotation, and statistical analysis of genetic alterations based on SNV/small indel techniques. RNA sequencing was additionally performed on eight selected patients, revealing no clinically significant gene fusions or mutations. Thus, the primary focus of this research is the presentation of DNA sequencing results. 2.3 Treatment regimens and efficacy assessments All patients diagnosed with DLBCL were uniformly treated with the frontline standard R-CHOP ± X chemotherapy protocol. A total of eight patients underwent consolidation with autologous hematopoietic stem cell transplantation (auto-HSCT). The second-line treatment options comprised gemcitabine + dexamethasone + cisplatin (GDP), cisplatin + cytarabine + dexamethasone (DHAP), polatuzumab vedotin + rituximab + bendamustine (Pola + BR), rituximab + bendamustine (BR), CD19-CAR-T and auto-HSCT, and other treatments. Other therapeutic agents included BTK inhibitors (zanubrutinib or orelabrutinib), XPO1 inhibitors (selinexor), IMiDs (lenalidomide), and demethylation agents (decitabine). Each patient was evaluated for therapeutic response through imaging studies, including CT, MRI, or PET/CT scans, with the response categorized as CR, PR, SD, or PD per the 2014 Lugano classification at completion of protocol therapy. The key criteria for response assessment are as follows ( 4 ). PET/CT-based response: CR: Deauville score 1, 2, or 3 with or without a residual mass; PR: Deauville score 4 or 5 with reduced uptake compared with baseline; SD: Deauville score 4 or 5 with no significant change in FDG uptake from baseline; PD: Deauville score 4 or 5 with an increase in intensity of uptake from baseline and/or new FDG-avid foci consistent with lymphoma. CT-based response: CR: Target nodes/nodal masses must regress to 1.5 cm in LDi, with no extralymphatic sites of disease; PR: ≥50% decrease in SPD of up to six target measurable nodes and extranodal sites; SD: <50% decrease from baseline in SPD of up to six dominant, measurable nodes and extranodal sites; no criteria for progressive disease are met; PD: Target nodes/nodal masses increase by 50% from PPD nadir or clear progression of preexisting nonmeasured lesions. 2.4 Follow-up All patients were continuously monitored and followed up until October 30, 2024. The follow-up data were collected by reviewing the inpatient and outpatient medical files, as well as through phone follow-ups, and used to determine PFS, PD, and OS. 2.5 Statistical analysis The data were analyzed using SPSS 26.0 and R-3.6.1 statistical programs. The ComplexHeatmap R package was applied to generate heatmaps. Comparisons of baseline clinical characteristics between two samples was conducted using the chi-square test or Fisher’s exact probability method to calculate the P values. OS and PFS were analyzed using the Kaplan–Meier method, accompanied by the log-rank test. Multivariate analysis of prognostic factors was carried out using the Cox proportional hazards regression model. P < 0.05 was considered significant. 3. RESULTS 3.1 Clinical features Fifty-nine newly diagnosed DLBCL patients were included in the study, with 34 males (57.6%) and 25 females (42.4%) with a median age of 61 (38–76) years. Thirty patients (50.8%) were aged 60 or older. All patients had an ECOG score of less than two. According to the International Prognostic Index (IPI), 25 patients (42.4%) had an IPI score ≤ 2, and 34 patients (57.6%) had an IPI score > 2. Twenty-one patients (35.6%) presented with B symptoms. Based on the Ann Arbor staging system, 3 patients (5.1%) were in stage I, 11 patients (18.6%) were in stage II, 8 patients (13.6%) were in stage III, and 37 patients (62.7%) were in stage IV. According to the cell-of-origin (COO) classification, 25 patients (42.4%) were classified as germinal center B-cell (GCB) and 34 patients (57.6%) as non-GCB. Thirty-seven patients (62.7%) had LDH levels above the normal range. Thirty-eight patients (64.4%) had extranodal disease, and 21 patients (35.6%) had disease confined to the nodal regions. At the last follow-up, 28 patients (47.5%) had achieved a CR, 9 patients (15.3%) a PR, 10 patients (16.9%) had SD, and 12 patients (20.3%) had PD. The median follow-up duration in this study was 18 ( 2 – 35 ) months, with 14 patients (23.7%) deceased and 45 patients (76.3%) surviving (Table 1 ). Table 1 Baseline clinical characteristics of enrolled patients. Characteristics Gender Male 34 (57.6%) Female 25 (42.4%) Age ≤ 60 years 29(49.2%) >60 years 30 (50.8%) IPI score ≤ 2 25(42.4%) >2 34(57.6%) Ann Arbor Stage Ⅰ 3(5.1%) , Ⅱ 11 (18.6%) Ⅲ 8(13.6%) Ⅳ 37(62.7%) B symptoms 21 (35.6%) Extranodal disease 38(64.4%) Cell of orgin GCB 25(42.4%) Non-GCB 34(57.6%) LDH>240U/L 37(62.7%) Treatment efficacy CR 28(47.5%) PR 9(15.3%) SD 10(16.9%) PD 12(20.3%) Status Survival 45(76.3%) Death 14 (23.7%) 3.2 Gene mutation profiling This investigation utilized a targeted sequencing approach with a panel of 114 genes commonly mutated in B-cell lymphoma. Each of the 59 DLBCL patients examined presented with at least one mutation, with missense mutations being the most common. A median mutation count of four (range, 1–9) was detected across all cases, and 32 patients (54.2%) had four or more mutations. Statistical analysis was conducted on genes with a mutation frequency exceeding 10%, which included TP53 (37.3%), KMT2D (27.1%), CD79B (25.4%), PIM1 (22.0%), TNFAIP3 (18.6%), MYD88 (16.9%), IRF4 (15.3%), B2M (13.6%), TNFRSF14 (11.9%), CREBBP (10.2%), and SOCS1 (10.2%) (Fig. 1 ). Based on the functional analysis summary reported in the literature( 29 ), we further stratified the mutations observed in our 59 DLBCL cases into eight functional categories: epigenetic modifiers, signal transduction, DNA damage response, apoptosis-related genes, immune escape pathways, transcription factors, cell cycle regulation, and splicing factors. Mutations linked to signal transduction were the most prevalent (38.2%), followed by those affecting epigenetic processes (17.3%). Mutations on genes impacting the DNA damage response were also notably high (10.5%) (Table 2 ). Meanwhile, we conducted statistical analysis of clinical features and some gene mutations, but the result was negative (Table 3 ) Table 2 Pathway enrichment analysis Genes classified and function pathway Mutant genes Frequency (n = 247) Epigenetic DNA methylation TET2 2.4% Histone methylation EZH2, KMT2C, KMT2C, EP300 10.9% Histone acetylation CREBBP 2.4% Chromatin remodeler ARID1A, ARID1B 1.6% Signal transduction RAS-­MAPK pathway BRAF, DUSP2 2.4% NF­kβ pathway CARD11, NFKBIE, TNFAIP3, IKBKB 6.1% Cytokine receptor CXCR4, TNFRSF14 4.6% NOTCH pathway DTX1, NOTCH1, NOTCH2 4.6% JAK–STAT pathway SOCS1, STAT3 3.6% PI3K­AKT pathway PIK3CD, FOXO1, ITPKB, SGK1 2.4% BCR/TLR CD79B, MYD88, LYN 10.9% Other pathways GNA13, MEF2B 3.6% DNA damage response ATM, TP53 10.5% Immune escape B2M, CD58, CD70, CIITA 5.7% Apoptosis related genes FAS, MYC 3.2% Cell cycle regulation BTG1, BTG1, CCND3, PIM1 10.1% Transcription factor IRF4, PRDM1 6.1% Splicing factor SF3B1 0.8% Other DDX3X, TBL1XR1, IGLL5, KLHL6, MPEG1, MYOM2, POSTN, SIN3A, TMSB4X, ZFP36L1 8.1% Table 3 Correlations between certain clinical features and genetic mutations Characteristics Total Patients Number of mutant genes>4 (n = 32) TP53 Mutated (n = 22) KMT2D Mutated (n = 16) χ² P Gender 0.800 0.371 Male 34 15 12 8 Female 25 17 10 8 Age 0.138 0.711 ≤ 60 years 29 15 11 7 >60 years 30 17 11 9 IPI score 0.118 0.731 ≤ 2 25 14 9 7 >2 34 18 13 9 Ann Arbor Stage 0.047 0.829 Ⅰ+Ⅱ 11 8 7 5 Ⅲ+Ⅳ 45 24 15 11 B symptoms 0.010 0.920 yes 21 11 8 6 no 38 21 14 10 Extranodal disease 0.211 0.646 yes 38 20 13 11 no 21 12 9 5 Cell of orgin 0.474 0.491 GCB 25 13 10 7 Non-GCB 34 19 12 9 LDH 0.054 0.816 >240U/L 37 19 13 10 ≤ 240U/L 22 13 9 6 3.3 Survival analysis based on clinical features We next conducted survival analysis based on the clinical characteristics of the 59 DLBCL patients included in this study. Results demonstrated that the presence of B symptoms (P = 0.031, P = 0.041), an IPI score > 2 (P = 0.026, P = 0.021), and a suboptimal response (P = 0.000, P = 0.000) were associated with poorer OS and PFS. No transplantation was associated with poorer OS (P = 0.036), and it showed a trend toward worse PFS (P = 0.054) compared to transplanted patients. No correlation with OS or PFS was observed for gender (P = 0.368, P = 0.321), age (P = 0.163, P = 0.157), COO (P = 0.536, P = 0.622), and Ann Arbor Stage (P = 0.385, P = 0.299) (Fig. 2 ). Prognostic indicators with P < 0.05 in univariate analysis were included in multivariate Cox regression analysis. Results showed that IPI (P = 0.008, HR = 8.591, 95% CI: 1.754–42.070) and therapeutic efficacy (P = 0.000, HR = 4.878, 95% CI: 2.220-10.719) were independent risk factors for OS. Likewise, both IPI (P = 0.014, HR = 7.581, 95% CI: 1.517–37.880) and therapeutic efficacy (P = 0.000, HR = 5.338, 95% CI: 2.443–11.660) were also independent risk factors for PFS (Table 4 ). Table 4 Multivariate analysis of prognostic risk factors of PFS and OS. Parameter Multivariate analysis HR (95% CI ) P value OS IPI>2 HR 8.591 (95%CI: 1.754–42.070) 0.008 Failure to achieve PR HR 4.878 (95%CI: 2.220-10.719) 0.000 PFS IPI>2 HR 7.581 ( 95%CI: 1.517–37.880) 0.014 Failure to achieve PR HR 5.338 ( 95%CI: 2.443–11.660) 0.000 3.4 Survival analysis for frequently mutated genes Survival analyses based on the mutation profiles of our DLBCL patient cohort demonstrated that having more than four gene mutations correlates with diminished OS and PFS (P = 0.019 and P = 0.015, respectively). In turn, a significant correlation with adverse OS and PFS outcomes was found for mutations in TP53 (P = 0.024, P = 0.006) and for harboring a mutant KMT2D gene in combination with more than four mutations (P = 0.009, P = 0.016). Of note, the effect of TP53 mutations on OS and PFS remained unchanged regardless of the number of co-mutated genes. In contrast, mutations in IRF4 are associated with improved OS (P = 0.048), evidencing also a trend toward better PFS (P = 0.063), whereas no significant correlations with either OS or PFS were observed for CD79B (P = 0.421, P = 0.264), PIM1 (P = 0.900, P = 0.783), TNFAIP3 (P = 0.798, P = 0.886), and MYD88 (P = 0.970, P = 0.794) (Fig. 3 ). Prognostic indicators with P < 0.05 in univariate analysis were included in multivariate Cox regression analysis. Results showed that more than four gene mutations (P = 0.005, HR = 6.936, 95% CI: 1.893–21.637), TP53 mutation (P = 0.002, HR = 5.175, 95% CI: 2.874–19.935), and KMT2D mutation in combination with more than four mutations (P = 0.000, HR = 3.357, 95% CI: 4.470-34.513) were independent risk factors for OS. Likewise, having more than four gene mutations (P = 0.002, HR = 7.670, 95% CI: 3.394–36.329) and TP53 mutation (P = 0.000, HR = 3.378, 95% CI: 2.169–31.540) were also independent risk factors for PFS (Table 5 ). Table 5 Multivariate analysis of prognostic risk factors of PFS and OS. Parameter Multivariate analysis HR (95% CI ) P value OS Number of mutant genes>4 HR 6.936 ( 95% CI: 1.893–21.637) 0.005 TP53 postive HR 5.175 (95% CI: 2.874–19.935) 0.002 KMT2D postive and number of mutant genes>4 HR 3.357 (95% CI: 4.470-34.513) 0.000 PFS Number of mutant genes>4 HR 7.670 (95% CI: 3.394–36.329) 0.002 TP53 postive HR 3.378 (95% CI: 2.169–31.540) 0.000 4. DISCUSSION DLBCL represents a highly heterogeneous group of malignant tumors. With the extensive adoption of NGS technology, its role in the diagnosis, treatment selection, and prognosis assessment of DLBCL has gained wide recognition. On the other hand, although the introduction of CD20 monoclonal antibodies and intensified chemotherapy regimens has markedly improved patient survival, a fraction of patients still encounter relapse or refractory disease. Therefore, it is imperative to clarify the clinical features and molecular mechanisms that are pertinent to DLBCL prognosis. Accordingly, this study aims to investigate the clinical and genetic predictors of therapeutic efficacy and prognosis in DLBCL by incorporating clinical and NGS data from 59 DLBCL patients newly diagnosed at our hospital. Through the analysis of gene expression profiles and mutation statuses, we seek to achieve a deeper comprehension of the molecular mechanisms underlying DLBCL and to offer more personalized treatment options for patients. We found that B symptoms represent, on univariate analysis, a significant risk factor for OS and PFS, which is in line with a previous assessment ( 34 ). The manifestation of B symptoms correlates with a higher tumor burden and a poorer prognosis. However, upon multivariate analysis, B symptoms were not found to impact either OS or PFS, suggesting that they should not be considered an independent prognostic factor based on our study alone. Further assessment in a larger cohort is thus necessary for confirmation. The IPI system is frequently utilized in clinical practice to assess the prognosis of patients diagnosed with DLBCL. Extensive research has substantiated that an IPI > 2 signifies an independent predictor of adverse outcomes for these patients (25; 27; 28). Consistent with this observation, in our patient cohort an IPI > 2 was correlated with diminished OS and PFS, as evidenced by both univariate and multivariate analyses. The therapeutic response of patients with DLBCL dictates their outcome, as those reaching CR are more likely to have longer OS and PFS ( 14 ), a finding replicated in our investigation. Although controversy remains regarding the use of auto-HSCT as first-line therapy for DLBCL, evidence suggests that consolidation therapy with auto-HSCT for patients suitable for transplantation can further improve PFS and OS (2; 7; 30). Our study results also indicate that in our limited sample size, patients undergoing auto-HSCT had a significantly longer OS —with a similar trend for PFS— than those who did not undergo transplantation. Therefore, auto-HSCT is still recommended for DLBCL patients with high-risk prognostic factors (1; 9; 12; 19). Of note, some studies suggested that the IPI score alone may not be apt to accurately predict the prognosis of DLBCL patients (8; 21; 31). Hence the importance of searching for new indicators that can more objectively assess the treatment response and prognosis of patients. The development of DLBCL is a complex process involving multiple gene mutations. Thus, a thorough understanding of the mutational landscape of DLBCL is essential for clarifying the biological mechanisms of tumor development and performing more accurate disease assessments and prognostic predictions. In recent years, extensive adoption of NGS technology, driven by its high sensitivity, large-scale throughput, cost efficiency, and quantitative capabilities, has significantly enriched our insight into the biological characteristics of DLBCL, expanding the boundaries of research into this condition (13; 16). Our investigation, applying NGS on newly diagnosed, untreated DLBCL patients, is aimed at evaluating the effect of genetic mutations on the prognosis of DLBCL. Consistent with findings from related research (3; 10; 32), our study identified several strong correlations between genetic mutations and patient outcomes. After establishing a median mutation count of four across all samples, additional statistical analyses demonstrated that irrespective of the particular mutations involved, tumors harboring mutations in excess of this median value predict inferior PFS and OS. A likely reason for this is that a spectrum of genetic mutations affecting diverse disease pathways leads to reduced responses to treatment, thereby resulting in poor prognoses. The tumor suppressor gene TP53 functions as a sensor of cellular stress, playing a vital role in DNA repair, cellular senescence, metabolism, and induction of cell death, thus preserving genomic stability and ensuring proper cellular function ( 22 ). Our research identified a TP53 mutation frequency of 37.3% in our study group, which is slightly higher compared to that reported by a prior study ( 3 ). Importantly, reinforcing the prognostic value of TP53 mutations in DLBCL, our survival analysis indicated that TP53 mutations independently contribute to poorer PFS and OS outcomes. Furthermore, mutations in TP53 may lead to a poorer prognosis irrespective of the number of other mutations. This suggests that TP53 mutation status can be employed to stratify DLBCL patients for prognostic evaluation and therapeutic decision-making. Additionally, a comparative analysis between patients with TP53 mutations and those with more than four mutations revealed that both groups exhibited poorer OS and PFS, yet no statistically significant difference was observed between them. This finding further validates our discovery that DLBCL cases harboring more than four mutations are indeed associated with poorer prognosis, a correlation that has been seldom reported. Although further research on larger sample sizes and longer follow-up are warranted, this suggests the need to pay close attention to patients with a high number of gene mutations, treating them with the same priority as those with TP53 mutations and possibly modifying traditional treatment strategies in an effort to improve the unfavorable prognosis of these patients. The KMT2D gene, alternatively known as MLL2 or MLL4, encodes a histone-lysine N-methyltransferase responsible for methylating histone H3 to regulate gene transcription. The presence of KMT2D mutations was reported to correlate with negative prognosis in DLBCL ( 11 ), a result not echoed in our investigation. However, our subgroup analysis has uniquely revealed that patients with mutations in KMT2D and at least four other genes have a significantly poorer prognosis than those with mutations in KMT2D and no more than three other genes. Hence, we propose that despite a possible correlation between the number of gene mutations in DLBCL and its prognosis, it is crucial to specifically distinguish the mutated genes to accurately identify those that impact prognosis. Our research delved deeper into the prognostic significance of additional high-frequency mutated genes and clinical features. Interferon regulatory factor 4 (IRF4) rearrangement defines one of the subtypes in the WHO classification of lymphomas. Our findings indicate that mutations in IRF4 enhance patient OS and suggest also a positive trend for PFS, aligning with the consensus documented in the literature ( 20 ). This offers critical prognostic insights for clinical management, underscoring the need for vigilant monitoring in our ongoing clinical work. In contrast, CD79B, PIM1, TNFAIP3, and MYD88 were found to have no correlation with patient outcomes in the present study. As this diverges from findings in other studies (15; 33; 35), we speculate that the discrepancy may be due to our limited sample size and the brief duration of follow-up. This study has certain limitations. First, it is a retrospective study with a relatively small sample size. Second, the follow-up period is relatively short. Consequently, we plan to expand the sample size and extend the follow-up period to conduct prospective clinical studies to validate these findings. Future studies will include functional validation of the identified genetic alterations in DLBCL cell lines. We further aim to develop a novel prognostic model that can robustly identify high-risk DLBCL patients at the genetic level and explore new treatment strategies for these patients. Declarations Acknowledgements We would like to express our sincere gratitude to all individuals who contributed to this research project and the preparation of this manuscript. We are grateful to Health Commission of the Guangxi Zhuang Autonomous Region for the financial support [No: Z20190628]. This research would not have been possible without their generous funding. Funding information This work was funded by the Health Commission of the Guangxi Zhuang Autonomous Region (Grant/Award Number: Z20190628). Clinical trial number: not applicable Availability of data and materials The datasets used and analyzed in the current study are available upon reasonable request from the corresponding author. In order to better share the data, we have uploaded it to the NCBI database, with the website address and accession number being as follows: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1246145 Authors’ contributions Xianyi Wu designed the study and drafted the manuscript. Jie Zhu and Taohua Deng collected the date and performed the statistical data analysis. Meilian Qin, Wenyuan Lin, Fujun Qu, and Fang Jiang provided the study material or patients in this study. Xiaotao Wang revised the manuscript. All authors contributed to the development of the manuscript and approved the final version. Ethics approval and consent to participate This study was performed in compliance with the Declaration of Helsinki, and approved by the Ethic Committee of the Affiliated Hospital of Guilin Medical University. Patient consent for publication The patient has been informed that the material to be published will include [describe the nature of the material, e.g., clinical history, radiological images, photographs, etc.]. And they have been given the opportunity to ask questions about the publication process and have received satisfactory answers. Competing interests The authors declare no conflict of interest References Berning P, Fekom M, Ngoya M, Goldstone AH, Dreger P, et al. Hematopoietic stem cell transplantation for DLBCL: a report from the European Society for Blood and Marrow Transplantation on more than 40,000 patients over 32 years. Blood Cancer J. 2024;14:106. Berning P, Fekom M, Ngoya M, Goldstone AH, Dreger P et al. 2024. Hematopoietic stem cell transplantation for DLBCL: a report from the European Society for Blood and Marrow Transplantation on more than 40,000 patients over 32 years. Blood Cancer J 14. Chapuy B, Stewart C, Dunford AJ, Kim J, Kamburov A, et al. 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Cancer. 2019;125:4417–25. Jelicic J, Larsen TS, Maksimovic M, Trajkovic G. Available prognostic models for risk stratification of diffuse large B cell lymphoma patients: a systematic review. Crit Rev Oncol Hematol. 2019;133:1–16. Koviazin AK, Filatova LV, Zyuzgin IS, Artemyeva AS, Poliatskin IL, et al. The significance of upfront autologous stem cell transplantation for high-intermediate/high-risk stage IV diffuse large B-cell lymphoma. Cancer Rep (Hoboken). 2023;6:e1786. Lacy SE, Barrans SL, Beer PA, Painter D, Smith AG, et al. Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report. Blood. 2020;135:1759–71. Liu QX, Zhu Y, Yi HM, Shen YG, Wang L, et al. KMT2D mutations promoted tumor progression in diffuse large B-cell lymphoma through altering tumor-induced regulatory T cell trafficking via FBXW7-NOTCH-MYC/TGF-beta1 axis. Int J Biol Sci. 2024;20:3972–85. Ma J, Sun S, Hu Y, Wu M, Shen L, et al. Novel conditioning regimen in upfront autologous stem cell transplantation in high-risk DLBCL. Bone Marrow Transpl. 2022;57:1612–4. Mendeville M, Roemer MGM, Los-de Vries GT, Chamuleau MED, de Jong D, Ylstra B. The path towards consensus genome classification of diffuse large B-cell lymphoma for use in clinical practice. Front Oncol. 2022;12:970063. Ng DZ, Lee CY, Lam WW, Tong AK, Tan SH, et al. Prognostication of diffuse large B-cell lymphoma patients with Deauville score of 3 or 4 at end-of-treatment PET evaluation: a comparison of the Deauville 5-point scale and the DeltaSUVmax method. Leuk Lymphoma. 2022;63:256–9. Ning FY, Wang HF, Liang ZY, Lan JP. Overexpression Inhibits Diffuse Large B-Cell Lymphoma Progression by Promoting Autophagy through TLR4/MyD88/NF-κB Signaling Pathway. Discov Med. 2024;36:1627–40. Ozcalimli A, Erdogdu IH, Turgutkaya A, Yavasoglu I, Doger FK, Bolaman AZ. The evaluation of gene mutation profiles by next-generation sequencing in diffuse large B-cell lymphoma. Int J Lab Hematol. 2023;45:310–6. Pasqualucci L, Dalla-Favera R. Genetics of diffuse large B-cell lymphoma. Blood. 2018;131:2307–19. Poletto S, Novo M, Paruzzo L, Frascione PMM, Vitolo U. Treatment strategies for patients with diffuse large B-cell lymphoma. Cancer Treat Rev. 2022;110:102443. Puckrin R, Sterrett R, Chua N, Owen C, Duggan P, et al. Consolidative Autotransplantation Achieves High Cure Rates in Adverse-Risk Large B Cell Lymphoma. Transpl Cell Ther. 2023;29:763e1. e5. Ramis-Zaldivar JE, Gonzalez-Farre B, Balague O, Celis V, Nadeu F, et al. Distinct molecular profile of IRF4-rearranged large B-cell lymphoma. Blood. 2020;135:274–86. Ruppert AS, Dixon JG, Salles G, Wall A, Cunningham D, et al. International prognostic indices in diffuse large B-cell lymphoma: a comparison of IPI, R-IPI, and NCCN-IPI. Blood. 2020;135:2041–8. Schaafsma E, Takacs EM, Kaur S, Cheng C, Kurokawa M. Predicting clinical outcomes of cancer patients with a p53 deficiency gene signature. Sci Rep. 2022;12:1317. Sehn LH, Gascoyne RD. Diffuse large B-cell lymphoma: optimizing outcome in the context of clinical and biologic heterogeneity. Blood. 2015;125:22–32. Sehn LH, Salles G. Diffuse Large B-Cell Lymphoma. N Engl J Med. 2021;384:842–58. Sehn LH, Salles G. Diffuse Large B-Cell Lymphoma. Reply. N Engl J Med. 2021;384:2262. Shi YF, Xu Y, Shen HF, Jin J, Tong HY, Xie WZ. 2024. Advances in biology, diagnosis and treatment of DLBCL. Ann Hematol. Susanibar-Adaniya S, Barta SK. 2021 Update on Diffuse large B cell lymphoma: A review of current data and potential applications on risk stratification and management. Am J Hematol. 2021;96:617–29. Tilly H, Morschhauser F, Sehn LH, Friedberg JW, Trneny M, et al. Polatuzumab Vedotin in Previously Untreated Diffuse Large B-Cell Lymphoma. N Engl J Med. 2022;386:351–63. Wang L, Yang L, Guan F, Chen J, Cheng Y, et al. TP53 and KMT2D mutations associated with worse prognosis in peripheral T-cell lymphomas. Cancer Med. 2024;13:e70027. Wen Q, Gao L, Xiong JK, Li Q, Wang SB, et al. High-dose Chemotherapy Combined with Autologous Hematopoietic Stem Cell Transplantation as Frontline Therapy for Intermediate/High-risk Diffuse Large B Cell Lymphoma. Curr Med Sci. 2021;41:465–73. Westin J, Sehn LH. CAR T cells as a second-line therapy for large B-cell lymphoma: a paradigm shift? Blood. 2022;139:2737–46. Wright GW, Huang DW, Phelan JD, Coulibaly ZA, Roulland S, et al. A Probabilistic Classification Tool for Genetic Subtypes of Diffuse Large B Cell Lymphoma with Therapeutic Implications. Cancer Cell. 2020;37:551–68. e14. Xu PP, Shen R, Shi ZY, Cheng S, Wang L, et al. The Prognostic Significance of CD79B Mutation in Diffuse Large B-Cell Lymphoma: A Meta-analysis and Systematic Literature Review. Clin Lymphoma Myeloma Leuk. 2022;22:e1051–8. e1. Yin T, Qi L, Zhou Y, Kong F, Wang S, et al. CD5 + diffuse large B-cell lymphoma has heterogeneous clinical features and poor prognosis: a single-center retrospective study in China. J Int Med Res. 2022;50:3000605221110075. Zhang H, Lu Y, Zhang T, Guan Q, Wang X, et al. PIM1 genetic alterations associated with distinct molecular profiles, phenotypes and drug responses in diffuse large B-cell lymphoma. Clin Transl Med. 2022;12:e808. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6665588","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475002721,"identity":"ede7fda8-9b85-442a-b0f5-e98173b74e5d","order_by":0,"name":"Xianyi Wu","email":"","orcid":"","institution":"The Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianyi","middleName":"","lastName":"Wu","suffix":""},{"id":475002723,"identity":"8d21037f-1189-4211-ac21-35f8fa9ea327","order_by":1,"name":"Jie Zhu","email":"","orcid":"","institution":"Graduate College of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhu","suffix":""},{"id":475002726,"identity":"dab79e54-df54-47b4-b24e-cf4864480529","order_by":2,"name":"Taohua Deng","email":"","orcid":"","institution":"The Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Taohua","middleName":"","lastName":"Deng","suffix":""},{"id":475002728,"identity":"cbd5154c-8dab-4b68-a6cb-25d15f29e051","order_by":3,"name":"Meilian Qin","email":"","orcid":"","institution":"Graduate College of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meilian","middleName":"","lastName":"Qin","suffix":""},{"id":475002730,"identity":"8fd472d9-d5f5-48e8-8e9b-89c8ca5c6448","order_by":4,"name":"Wenyuan Lin","email":"","orcid":"","institution":"The Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenyuan","middleName":"","lastName":"Lin","suffix":""},{"id":475002731,"identity":"0ae2092c-aec0-4f8f-8061-c6293614e422","order_by":5,"name":"Fujun Qu","email":"","orcid":"","institution":"The Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fujun","middleName":"","lastName":"Qu","suffix":""},{"id":475002733,"identity":"b231bd89-315d-4012-9b33-c0cd359e7c35","order_by":6,"name":"Fang Jiang","email":"","orcid":"","institution":"The Affiliated Hospital of Guilin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Jiang","suffix":""},{"id":475002734,"identity":"6401dbc8-206e-4452-85ef-aaa54b4e5ed7","order_by":7,"name":"Xiaotao Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYLACiQIbHn72BqLVMzM2SBikyUj2HCBFC4PBYRuDGw5EajC4kX/8gYXBeR6GGwyMHz7mEKPlzGGQw27zMM5uYJacuY0ILWbHmyFamGUOsDHzEqXlMNj753jYJBKI1QKx5QAPD9Fa7M8cNpwhYZDMI8FzsJk4v0jOSHzwWaLCzt7+ePPBDx+J0QICzBJgChg/RAPGD8SrHQWjYBSMgpEIAPJTMjnzMFOnAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital of Guilin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaotao","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-05-14 15:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6665588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6665588/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85348777,"identity":"03950784-2f57-4dd6-bee3-587f5b4b1b5b","added_by":"auto","created_at":"2025-06-25 02:28:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1169684,"visible":true,"origin":"","legend":"\u003cp\u003eMutational landscape and genetic subtype distribution of DLBCL.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6665588/v1/fad2df5b900b19e8b17b4fe3.png"},{"id":85348784,"identity":"734b45f9-4810-41d4-b389-fbce34b684b4","added_by":"auto","created_at":"2025-06-25 02:28:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3627739,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves. B symptoms, IPI \u0026gt;2, failure to achieve PR, and non-transplantation suggested worse OS and PFS.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6665588/v1/8c069b08e3356488f50f165f.png"},{"id":85349874,"identity":"96af8efa-ed7a-4ae8-a12c-d15806694db0","added_by":"auto","created_at":"2025-06-25 02:44:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3409199,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier survival curves. Number of mutant genes \u0026gt;4, TP53 mutation, and KMT2D mutation concurrent with \u0026gt;4 mutant genes suggested worse OS and PFS. IRF4 negative mutation status suggested worse OS and a tendency for poorer PFS.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6665588/v1/8767ad2f922883e65cc536e9.png"},{"id":91056485,"identity":"3222456d-334d-492d-b89f-2016990fae91","added_by":"auto","created_at":"2025-09-11 08:02:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9208959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6665588/v1/37188d5a-1a0b-4b36-80ff-504089606fd6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Analysis of Diffuse Large B-Cell Lymphoma Patients Based on Clinical Characteristics, TP53 Mutation Status, and Number of Co-mutated Genes","fulltext":[{"header":"1. BACKGROUND","content":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) stands as the most prevalent subtype of non-Hodgkin lymphoma (NHL) in adults, responsible for approximately 35\u0026ndash;40% of NHL cases (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). It is a disease characterized by substantial heterogeneity, with marked variations in biological, pathological, clinical, and genomic characteristics among patients (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The development of DLBCL is highly complex, and involves altered oxidative phosphorylation and differential expression of genes involved in B-cell receptor (BCR) signaling and host inflammatory responses (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The pathways affected play a critical role in the development and maintenance of DLBCL, as well as in the response to treatment. At present, the R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) regimen is the standard first-line treatment for DLBCL, with a cure rate higher than 60% across all patients (23; 24). However, up to 40% of patients exhibit refractory disease, and they are prone to relapse after remission. The prognosis for this group is generally poor, and most patients ultimately succumb to lymphoma. Consequently, these patients have become the primary focus of current research (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, the application of next-generation sequencing (NGS) technology has led to extensive genomic research on DLBCL. This research progress has aided the genetic classification of DLBCL subtypes, which enhanced therapeutic efficacy by expanding treatment approaches to include small-molecule targeted drugs in addition to the R-CHOP regimen (18; 32). Since there is relatively little research on the impact of co-mutated genes on treatment response and prognosis of DLBCL patients, this study analyzed potential alterations in 114 genes related to B-cell lymphoma in 59 newly diagnosed DLBCL patients. The findings presented herein reveal new insights into the association between co-mutated genes and clinical characteristics, and their impact on treatment response and prognosis.\u003c/p\u003e"},{"header":"2. PATIENTS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eThe current study involved 59 patients with DLBCL who were treated at the Hematology Clinic of the Affiliated Hospital of Guilin Medical University from January 2022 through August 2024. All patients provided written informed consent for the use of their data in scientific research. Each patient was diagnosed with DLBCL in accordance with the WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues, 5th Edition, utilizing histopathological biopsy and immunohistochemical staining methods. The immunohistochemical assessment included a panel of markers such as CD20, CD19, CD79b, CD3, CD10, BCL2, BCL6, Ki-67, CD5, CD30, CD21, CD23, MUM1, EBER, and TP53. All histological slides were reviewed and confirmed by two experienced pathologists. The criteria for inclusion encompassed individuals aged between 18 and 80 years, irrespective of gender, who were all newly diagnosed cases. Criteria for exclusion comprised patients lacking comprehensive clinical data or those who were lost to follow-up; patients who did not undergo treatment; patients who had received radiotherapy or chemotherapy prior to enrollment; and patients with other hematological malignancies or malignant cachexia. Post-diagnosis, all patients were administered the standard first-line treatment and their responses to treatment were evaluated using PET-CT or CT scans. Patients who did not achieve a partial response (PR) were scheduled for second-line therapy or enrollment in clinical trials. The exclusion criteria for the study population included patients with incomplete clinical records or those who were lost to follow-up, individuals who had previously undergone radiotherapy or chemotherapy, and those with additional hematological malignancies or consumptive malignant diseases. Baseline clinical data, which comprised information on the patients\u0026rsquo; gender, age, lactate dehydrogenase (LDH) levels, Ann Arbor Stage, Eastern Cooperative Oncology Group performance status (ECOG PS), bone marrow biopsy results, B symptoms, treatment modalities, and therapeutic efficacy (encompassing PR, complete response [CR], stable disease [SD], and progressive disease [PD]), Lugano 2014(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)), were collected at the time of initial diagnosis. Progression-free survival (PFS) was defined as the duration from diagnosis until the first evidence of disease progression or death from any cause, while overall survival (OS) was defined as the time from diagnosis to the last follow-up visit or death from any cause. Informed consent was obtained from all patients, and the study was conducted under the approval of the Medical Ethics Committee of the Affiliated Hospital of Guilin Medical University, adhering strictly to the tenets of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 NGS sequencing\u003c/h2\u003e \u003cp\u003ePatient diagnosis was conducted at the Hematology laboratory of the Affiliated Hospital of Guilin Medical University by NGS sequencing of genomic DNA isolated from formalin-fixed and paraffin-embedded tumor sections. The gene panel was designed to include genes frequently altered in B-cell lymphomas. Genomic DNA was extracted from formalin-fixed paraffin-embedded tissues (FFPE) samples using a QIAamp Blood DNA Mini Kit (Cat No. 51104, QIAGEN, Hilden, Germany) and 100 ng of genomic DNA was further used to prepare a captured library using Enzyme Plus Library Prep kit (Cat. No C11111, iGeneTech, Shanghai, China) and TargetSeq One Hyb \u0026amp; Wash Kit (Module C, for Illumina) (Cat. No C10331, iGeneTech, Shanghai, China). Targeted sequencing of B-NHL related genes was performed at Shanghai Rightongene Biotechnology Co, Ltd. (Shanghai, China) on the Hiseq2000 (Illumina, USA) sequencing platform with 2 \u0026times; 150 bp pair-end protocol (HiSeq Cluster Kit v4, Illumina, USA). The final library was diluted to a final DNA concentration of 2 nM, which was detected using Qubit\u0026trade; dsDNA Quantification Assay Kits (Q32851, Invitrogen, Thermofisher, USA). Denature and dilute the final libraries to a final DNA concentration of 20 pM for cluster generation. The original sequencing was aligned with the human reference genome GRCh37. Single nucleotide variations (SNVs) and insertion and deletion (Indels) were screened by Shanghai Rightongene Biotechnology Co., Ltd. (Shanghai, China) based on the filtering conditions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) reads base quality\u0026thinsp;\u0026lt;\u0026thinsp;20; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) variants on the positive-strand and negative-strand were inconsistent; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) variant allele frequency (VAF)\u0026thinsp;\u0026lt;\u0026thinsp;1% and individual mutant reads\u0026thinsp;\u0026lt;\u0026thinsp;20; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) dbSNP (v147) sites that were not existed in COSMIC database; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) SNPs (single nucleotide polymorphisms) or Indels (insertions and deletions) with a mutation allele frequency (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.001 in databases of 1,000 genomes project, 1,000 genome East Asian, ExAC all, or ExAC East Asian were removed. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) missense mutations were predicted not deleterious by Bioinformatics Tools (sift\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Polyphen2_HVAR_pred\u0026thinsp;\u0026lt;\u0026thinsp;0.447, and CADD_phred\u0026thinsp;\u0026lt;\u0026thinsp;15); (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) mutations were predicted to be synonymous. DNA sequencing was conducted on all patients enrolled in the study, with detection, annotation, and statistical analysis of genetic alterations based on SNV/small indel techniques. RNA sequencing was additionally performed on eight selected patients, revealing no clinically significant gene fusions or mutations. Thus, the primary focus of this research is the presentation of DNA sequencing results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Treatment regimens and efficacy assessments\u003c/h2\u003e \u003cp\u003eAll patients diagnosed with DLBCL were uniformly treated with the frontline standard R-CHOP\u0026thinsp;\u0026plusmn;\u0026thinsp;X chemotherapy protocol. A total of eight patients underwent consolidation with autologous hematopoietic stem cell transplantation (auto-HSCT). The second-line treatment options comprised gemcitabine\u0026thinsp;+\u0026thinsp;dexamethasone\u0026thinsp;+\u0026thinsp;cisplatin (GDP), cisplatin\u0026thinsp;+\u0026thinsp;cytarabine\u0026thinsp;+\u0026thinsp;dexamethasone (DHAP), polatuzumab vedotin\u0026thinsp;+\u0026thinsp;rituximab\u0026thinsp;+\u0026thinsp;bendamustine (Pola\u0026thinsp;+\u0026thinsp;BR), rituximab\u0026thinsp;+\u0026thinsp;bendamustine (BR), CD19-CAR-T and auto-HSCT, and other treatments. Other therapeutic agents included BTK inhibitors (zanubrutinib or orelabrutinib), XPO1 inhibitors (selinexor), IMiDs (lenalidomide), and demethylation agents (decitabine). Each patient was evaluated for therapeutic response through imaging studies, including CT, MRI, or PET/CT scans, with the response categorized as CR, PR, SD, or PD per the 2014 Lugano classification at completion of protocol therapy. The key criteria for response assessment are as follows (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePET/CT-based response:\u003c/p\u003e \u003cp\u003eCR: Deauville score 1, 2, or 3 with or without a residual mass;\u003c/p\u003e \u003cp\u003ePR: Deauville score 4 or 5 with reduced uptake compared with baseline;\u003c/p\u003e \u003cp\u003eSD: Deauville score 4 or 5 with no significant change in FDG uptake from baseline;\u003c/p\u003e \u003cp\u003ePD: Deauville score 4 or 5 with an increase in intensity of uptake from baseline and/or new FDG-avid foci consistent with lymphoma.\u003c/p\u003e \u003cp\u003eCT-based response:\u003c/p\u003e \u003cp\u003eCR: Target nodes/nodal masses must regress to 1.5 cm in LDi, with no extralymphatic sites of disease;\u003c/p\u003e \u003cp\u003ePR: \u0026ge;50% decrease in SPD of up to six target measurable nodes and extranodal sites;\u003c/p\u003e \u003cp\u003eSD: \u0026lt;50% decrease from baseline in SPD of up to six dominant, measurable nodes and extranodal sites; no criteria for progressive disease are met;\u003c/p\u003e \u003cp\u003ePD: Target nodes/nodal masses increase by 50% from PPD nadir or clear progression of preexisting nonmeasured lesions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Follow-up\u003c/h2\u003e \u003cp\u003eAll patients were continuously monitored and followed up until October 30, 2024. The follow-up data were collected by reviewing the inpatient and outpatient medical files, as well as through phone follow-ups, and used to determine PFS, PD, and OS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data were analyzed using SPSS 26.0 and R-3.6.1 statistical programs. The ComplexHeatmap R package was applied to generate heatmaps. Comparisons of baseline clinical characteristics between two samples was conducted using the chi-square test or Fisher\u0026rsquo;s exact probability method to calculate the P values. OS and PFS were analyzed using the Kaplan\u0026ndash;Meier method, accompanied by the log-rank test. Multivariate analysis of prognostic factors was carried out using the Cox proportional hazards regression model. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinical features\u003c/h2\u003e \u003cp\u003eFifty-nine newly diagnosed DLBCL patients were included in the study, with 34 males (57.6%) and 25 females (42.4%) with a median age of 61 (38\u0026ndash;76) years. Thirty patients (50.8%) were aged 60 or older. All patients had an ECOG score of less than two. According to the International Prognostic Index (IPI), 25 patients (42.4%) had an IPI score\u0026thinsp;\u0026le;\u0026thinsp;2, and 34 patients (57.6%) had an IPI score\u0026thinsp;\u0026gt;\u0026thinsp;2. Twenty-one patients (35.6%) presented with B symptoms. Based on the Ann Arbor staging system, 3 patients (5.1%) were in stage I, 11 patients (18.6%) were in stage II, 8 patients (13.6%) were in stage III, and 37 patients (62.7%) were in stage IV. According to the cell-of-origin (COO) classification, 25 patients (42.4%) were classified as germinal center B-cell (GCB) and 34 patients (57.6%) as non-GCB. Thirty-seven patients (62.7%) had LDH levels above the normal range. Thirty-eight patients (64.4%) had extranodal disease, and 21 patients (35.6%) had disease confined to the nodal regions. At the last follow-up, 28 patients (47.5%) had achieved a CR, 9 patients (15.3%) a PR, 10 patients (16.9%) had SD, and 12 patients (20.3%) had PD. The median follow-up duration in this study was 18 (\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10 CR11 CR12 CR13 CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31 CR32 CR33 CR34\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) months, with 14 patients (23.7%) deceased and 45 patients (76.3%) surviving (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline clinical characteristics of enrolled patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29(49.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (50.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(42.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34(57.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnn Arbor Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(5.1%) ,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (18.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8(13.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37(62.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (35.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtranodal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38(64.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell of orgin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25(42.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-GCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34(57.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u0026gt;240U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37(62.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28(47.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9(15.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10(16.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(20.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurvival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45(76.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Gene mutation profiling\u003c/h2\u003e \u003cp\u003eThis investigation utilized a targeted sequencing approach with a panel of 114 genes commonly mutated in B-cell lymphoma. Each of the 59 DLBCL patients examined presented with at least one mutation, with missense mutations being the most common. A median mutation count of four (range, 1\u0026ndash;9) was detected across all cases, and 32 patients (54.2%) had four or more mutations. Statistical analysis was conducted on genes with a mutation frequency exceeding 10%, which included TP53 (37.3%), KMT2D (27.1%), CD79B (25.4%), PIM1 (22.0%), TNFAIP3 (18.6%), MYD88 (16.9%), IRF4 (15.3%), B2M (13.6%), TNFRSF14 (11.9%), CREBBP (10.2%), and SOCS1 (10.2%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the functional analysis summary reported in the literature(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), we further stratified the mutations observed in our 59 DLBCL cases into eight functional categories: epigenetic modifiers, signal transduction, DNA damage response, apoptosis-related genes, immune escape pathways, transcription factors, cell cycle regulation, and splicing factors. Mutations linked to signal transduction were the most prevalent (38.2%), followed by those affecting epigenetic processes (17.3%). Mutations on genes impacting the DNA damage response were also notably high (10.5%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Meanwhile, we conducted statistical analysis of clinical features and some gene mutations, but the result was negative (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePathway enrichment analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGenes classified and function pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMutant genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;247)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpigenetic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDNA methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTET2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistone methylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEZH2, KMT2C, KMT2C, EP300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistone acetylation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCREBBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromatin remodeler\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eARID1A, ARID1B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAS-\u0026shy;MAPK pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBRAF, DUSP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNF\u0026shy;kβ pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCARD11, NFKBIE, TNFAIP3, IKBKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCytokine receptor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCXCR4, TNFRSF14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNOTCH pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDTX1, NOTCH1, NOTCH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJAK\u0026ndash;STAT pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOCS1, STAT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePI3K\u0026shy;AKT pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePIK3CD, FOXO1, ITPKB, SGK1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCR/TLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCD79B, MYD88, LYN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther pathways\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGNA13, MEF2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDNA damage response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATM, TP53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eImmune escape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB2M, CD58, CD70, CIITA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eApoptosis related genes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAS, MYC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCell cycle regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBTG1, BTG1, CCND3, PIM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTranscription factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIRF4, PRDM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSplicing factor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSF3B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDDX3X, TBL1XR1, IGLL5, KLHL6, MPEG1, MYOM2, POSTN, SIN3A, TMSB4X, ZFP36L1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between certain clinical features and genetic mutations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Patients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of mutant genes\u0026gt;4 (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTP53 Mutated (n\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKMT2D Mutated (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eχ\u0026sup2;\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPI score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnn Arbor Stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅠ+Ⅱ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eⅢ+Ⅳ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtranodal disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell of orgin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-GCB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;240U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;240U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Survival analysis based on clinical features\u003c/h2\u003e \u003cp\u003eWe next conducted survival analysis based on the clinical characteristics of the 59 DLBCL patients included in this study. Results demonstrated that the presence of B symptoms (P\u0026thinsp;=\u0026thinsp;0.031, P\u0026thinsp;=\u0026thinsp;0.041), an IPI score\u0026thinsp;\u0026gt;\u0026thinsp;2 (P\u0026thinsp;=\u0026thinsp;0.026, P\u0026thinsp;=\u0026thinsp;0.021), and a suboptimal response (P\u0026thinsp;=\u0026thinsp;0.000, P\u0026thinsp;=\u0026thinsp;0.000) were associated with poorer OS and PFS. No transplantation was associated with poorer OS (P\u0026thinsp;=\u0026thinsp;0.036), and it showed a trend toward worse PFS (P\u0026thinsp;=\u0026thinsp;0.054) compared to transplanted patients. No correlation with OS or PFS was observed for gender (P\u0026thinsp;=\u0026thinsp;0.368, P\u0026thinsp;=\u0026thinsp;0.321), age (P\u0026thinsp;=\u0026thinsp;0.163, P\u0026thinsp;=\u0026thinsp;0.157), COO (P\u0026thinsp;=\u0026thinsp;0.536, P\u0026thinsp;=\u0026thinsp;0.622), and Ann Arbor Stage (P\u0026thinsp;=\u0026thinsp;0.385, P\u0026thinsp;=\u0026thinsp;0.299) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Prognostic indicators with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multivariate Cox regression analysis. Results showed that IPI (P\u0026thinsp;=\u0026thinsp;0.008, HR\u0026thinsp;=\u0026thinsp;8.591, 95% CI: 1.754\u0026ndash;42.070) and therapeutic efficacy (P\u0026thinsp;=\u0026thinsp;0.000, HR\u0026thinsp;=\u0026thinsp;4.878, 95% CI: 2.220-10.719) were independent risk factors for OS. Likewise, both IPI (P\u0026thinsp;=\u0026thinsp;0.014, HR\u0026thinsp;=\u0026thinsp;7.581, 95% CI: 1.517\u0026ndash;37.880) and therapeutic efficacy (P\u0026thinsp;=\u0026thinsp;0.000, HR\u0026thinsp;=\u0026thinsp;5.338, 95% CI: 2.443\u0026ndash;11.660) were also independent risk factors for PFS (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of prognostic risk factors of PFS and OS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPI\u0026gt;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 8.591 (95%CI: 1.754\u0026ndash;42.070)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailure to achieve PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 4.878 (95%CI: 2.220-10.719)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPI\u0026gt;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 7.581 ( 95%CI: 1.517\u0026ndash;37.880)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFailure to achieve PR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 5.338 ( 95%CI: 2.443\u0026ndash;11.660)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Survival analysis for frequently mutated genes\u003c/h2\u003e \u003cp\u003eSurvival analyses based on the mutation profiles of our DLBCL patient cohort demonstrated that having more than four gene mutations correlates with diminished OS and PFS (P\u0026thinsp;=\u0026thinsp;0.019 and P\u0026thinsp;=\u0026thinsp;0.015, respectively). In turn, a significant correlation with adverse OS and PFS outcomes was found for mutations in TP53 (P\u0026thinsp;=\u0026thinsp;0.024, P\u0026thinsp;=\u0026thinsp;0.006) and for harboring a mutant KMT2D gene in combination with more than four mutations (P\u0026thinsp;=\u0026thinsp;0.009, P\u0026thinsp;=\u0026thinsp;0.016). Of note, the effect of TP53 mutations on OS and PFS remained unchanged regardless of the number of co-mutated genes. In contrast, mutations in IRF4 are associated with improved OS (P\u0026thinsp;=\u0026thinsp;0.048), evidencing also a trend toward better PFS (P\u0026thinsp;=\u0026thinsp;0.063), whereas no significant correlations with either OS or PFS were observed for CD79B (P\u0026thinsp;=\u0026thinsp;0.421, P\u0026thinsp;=\u0026thinsp;0.264), PIM1 (P\u0026thinsp;=\u0026thinsp;0.900, P\u0026thinsp;=\u0026thinsp;0.783), TNFAIP3 (P\u0026thinsp;=\u0026thinsp;0.798, P\u0026thinsp;=\u0026thinsp;0.886), and MYD88 (P\u0026thinsp;=\u0026thinsp;0.970, P\u0026thinsp;=\u0026thinsp;0.794) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Prognostic indicators with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multivariate Cox regression analysis. Results showed that more than four gene mutations (P\u0026thinsp;=\u0026thinsp;0.005, HR\u0026thinsp;=\u0026thinsp;6.936, 95% CI: 1.893\u0026ndash;21.637), TP53 mutation (P\u0026thinsp;=\u0026thinsp;0.002, HR\u0026thinsp;=\u0026thinsp;5.175, 95% CI: 2.874\u0026ndash;19.935), and KMT2D mutation in combination with more than four mutations (P\u0026thinsp;=\u0026thinsp;0.000, HR\u0026thinsp;=\u0026thinsp;3.357, 95% CI: 4.470-34.513) were independent risk factors for OS. Likewise, having more than four gene mutations (P\u0026thinsp;=\u0026thinsp;0.002, HR\u0026thinsp;=\u0026thinsp;7.670, 95% CI: 3.394\u0026ndash;36.329) and TP53 mutation (P\u0026thinsp;=\u0026thinsp;0.000, HR\u0026thinsp;=\u0026thinsp;3.378, 95% CI: 2.169\u0026ndash;31.540) were also independent risk factors for PFS (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of prognostic risk factors of PFS and OS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of mutant genes\u0026gt;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 6.936 ( 95% CI: 1.893\u0026ndash;21.637)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53 postive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 5.175 (95% CI: 2.874\u0026ndash;19.935)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKMT2D postive and number of mutant genes\u0026gt;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 3.357 (95% CI: 4.470-34.513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of mutant genes\u0026gt;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 7.670 (95% CI: 3.394\u0026ndash;36.329)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP53 postive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR 3.378 (95% CI: 2.169\u0026ndash;31.540)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eDLBCL represents a highly heterogeneous group of malignant tumors. With the extensive adoption of NGS technology, its role in the diagnosis, treatment selection, and prognosis assessment of DLBCL has gained wide recognition. On the other hand, although the introduction of CD20 monoclonal antibodies and intensified chemotherapy regimens has markedly improved patient survival, a fraction of patients still encounter relapse or refractory disease. Therefore, it is imperative to clarify the clinical features and molecular mechanisms that are pertinent to DLBCL prognosis. Accordingly, this study aims to investigate the clinical and genetic predictors of therapeutic efficacy and prognosis in DLBCL by incorporating clinical and NGS data from 59 DLBCL patients newly diagnosed at our hospital. Through the analysis of gene expression profiles and mutation statuses, we seek to achieve a deeper comprehension of the molecular mechanisms underlying DLBCL and to offer more personalized treatment options for patients.\u003c/p\u003e \u003cp\u003eWe found that B symptoms represent, on univariate analysis, a significant risk factor for OS and PFS, which is in line with a previous assessment (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The manifestation of B symptoms correlates with a higher tumor burden and a poorer prognosis. However, upon multivariate analysis, B symptoms were not found to impact either OS or PFS, suggesting that they should not be considered an independent prognostic factor based on our study alone. Further assessment in a larger cohort is thus necessary for confirmation. The IPI system is frequently utilized in clinical practice to assess the prognosis of patients diagnosed with DLBCL. Extensive research has substantiated that an IPI\u0026thinsp;\u0026gt;\u0026thinsp;2 signifies an independent predictor of adverse outcomes for these patients (25; 27; 28). Consistent with this observation, in our patient cohort an IPI\u0026thinsp;\u0026gt;\u0026thinsp;2 was correlated with diminished OS and PFS, as evidenced by both univariate and multivariate analyses.\u003c/p\u003e \u003cp\u003eThe therapeutic response of patients with DLBCL dictates their outcome, as those reaching CR are more likely to have longer OS and PFS (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), a finding replicated in our investigation. Although controversy remains regarding the use of auto-HSCT as first-line therapy for DLBCL, evidence suggests that consolidation therapy with auto-HSCT for patients suitable for transplantation can further improve PFS and OS (2; 7; 30). Our study results also indicate that in our limited sample size, patients undergoing auto-HSCT had a significantly longer OS \u0026mdash;with a similar trend for PFS\u0026mdash; than those who did not undergo transplantation. Therefore, auto-HSCT is still recommended for DLBCL patients with high-risk prognostic factors (1; 9; 12; 19). Of note, some studies suggested that the IPI score alone may not be apt to accurately predict the prognosis of DLBCL patients (8; 21; 31). Hence the importance of searching for new indicators that can more objectively assess the treatment response and prognosis of patients.\u003c/p\u003e \u003cp\u003eThe development of DLBCL is a complex process involving multiple gene mutations. Thus, a thorough understanding of the mutational landscape of DLBCL is essential for clarifying the biological mechanisms of tumor development and performing more accurate disease assessments and prognostic predictions. In recent years, extensive adoption of NGS technology, driven by its high sensitivity, large-scale throughput, cost efficiency, and quantitative capabilities, has significantly enriched our insight into the biological characteristics of DLBCL, expanding the boundaries of research into this condition (13; 16).\u003c/p\u003e \u003cp\u003eOur investigation, applying NGS on newly diagnosed, untreated DLBCL patients, is aimed at evaluating the effect of genetic mutations on the prognosis of DLBCL. Consistent with findings from related research (3; 10; 32), our study identified several strong correlations between genetic mutations and patient outcomes. After establishing a median mutation count of four across all samples, additional statistical analyses demonstrated that irrespective of the particular mutations involved, tumors harboring mutations in excess of this median value predict inferior PFS and OS. A likely reason for this is that a spectrum of genetic mutations affecting diverse disease pathways leads to reduced responses to treatment, thereby resulting in poor prognoses.\u003c/p\u003e \u003cp\u003eThe tumor suppressor gene TP53 functions as a sensor of cellular stress, playing a vital role in DNA repair, cellular senescence, metabolism, and induction of cell death, thus preserving genomic stability and ensuring proper cellular function (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Our research identified a TP53 mutation frequency of 37.3% in our study group, which is slightly higher compared to that reported by a prior study (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Importantly, reinforcing the prognostic value of TP53 mutations in DLBCL, our survival analysis indicated that TP53 mutations independently contribute to poorer PFS and OS outcomes. Furthermore, mutations in TP53 may lead to a poorer prognosis irrespective of the number of other mutations. This suggests that TP53 mutation status can be employed to stratify DLBCL patients for prognostic evaluation and therapeutic decision-making. Additionally, a comparative analysis between patients with TP53 mutations and those with more than four mutations revealed that both groups exhibited poorer OS and PFS, yet no statistically significant difference was observed between them. This finding further validates our discovery that DLBCL cases harboring more than four mutations are indeed associated with poorer prognosis, a correlation that has been seldom reported. Although further research on larger sample sizes and longer follow-up are warranted, this suggests the need to pay close attention to patients with a high number of gene mutations, treating them with the same priority as those with TP53 mutations and possibly modifying traditional treatment strategies in an effort to improve the unfavorable prognosis of these patients.\u003c/p\u003e \u003cp\u003eThe KMT2D gene, alternatively known as MLL2 or MLL4, encodes a histone-lysine N-methyltransferase responsible for methylating histone H3 to regulate gene transcription. The presence of KMT2D mutations was reported to correlate with negative prognosis in DLBCL (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), a result not echoed in our investigation. However, our subgroup analysis has uniquely revealed that patients with mutations in KMT2D and at least four other genes have a significantly poorer prognosis than those with mutations in KMT2D and no more than three other genes. Hence, we propose that despite a possible correlation between the number of gene mutations in DLBCL and its prognosis, it is crucial to specifically distinguish the mutated genes to accurately identify those that impact prognosis.\u003c/p\u003e \u003cp\u003eOur research delved deeper into the prognostic significance of additional high-frequency mutated genes and clinical features. Interferon regulatory factor 4 (IRF4) rearrangement defines one of the subtypes in the WHO classification of lymphomas. Our findings indicate that mutations in IRF4 enhance patient OS and suggest also a positive trend for PFS, aligning with the consensus documented in the literature (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This offers critical prognostic insights for clinical management, underscoring the need for vigilant monitoring in our ongoing clinical work. In contrast, CD79B, PIM1, TNFAIP3, and MYD88 were found to have no correlation with patient outcomes in the present study. As this diverges from findings in other studies (15; 33; 35), we speculate that the discrepancy may be due to our limited sample size and the brief duration of follow-up.\u003c/p\u003e \u003cp\u003eThis study has certain limitations. First, it is a retrospective study with a relatively small sample size. Second, the follow-up period is relatively short. Consequently, we plan to expand the sample size and extend the follow-up period to conduct prospective clinical studies to validate these findings. Future studies will include functional validation of the identified genetic alterations in DLBCL cell lines. We further aim to develop a novel prognostic model that can robustly identify high-risk DLBCL patients at the genetic level and explore new treatment strategies for these patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals who contributed to this research project and the preparation of this manuscript. We are grateful to Health Commission of the Guangxi Zhuang Autonomous Region for the financial support [No: Z20190628]. This research would not have been possible without their generous funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Health Commission of the Guangxi Zhuang Autonomous Region (Grant/Award Number: Z20190628).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed in the current study are available upon reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003eIn order to better share the data, we have uploaded it to the NCBI database, with the website address and accession number being as follows:\u003c/p\u003e\n\u003cp\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/PRJNA1246145\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXianyi Wu designed the study and drafted the manuscript. Jie Zhu and Taohua Deng collected the date and performed the statistical data analysis. Meilian Qin, Wenyuan Lin, Fujun Qu, and Fang Jiang provided the study material or patients in this study. Xiaotao Wang revised the manuscript. All authors contributed to the development of the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in compliance with the Declaration of Helsinki, and approved by the Ethic Committee of the Affiliated Hospital of Guilin Medical University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patient has been informed that the material to be published will include [describe the nature of the material, e.g., clinical history, radiological images, photographs, etc.]. And they have been given the opportunity to ask questions about the publication process and have received satisfactory answers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBerning P, Fekom M, Ngoya M, Goldstone AH, Dreger P, et al. Hematopoietic stem cell transplantation for DLBCL: a report from the European Society for Blood and Marrow Transplantation on more than 40,000 patients over 32 years. Blood Cancer J. 2024;14:106.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerning P, Fekom M, Ngoya M, Goldstone AH, Dreger P et al. 2024. Hematopoietic stem cell transplantation for DLBCL: a report from the European Society for Blood and Marrow Transplantation on more than 40,000 patients over 32 years. 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Clin Transl Med. 2022;12:e808.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diffuse large B-cell lymphoma, clinical features, next generation sequencing, TP53, IRF4, KMT2D","lastPublishedDoi":"10.21203/rs.3.rs-6665588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6665588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe retrospectively analyzed 59 patients newly diagnosed with diffuse large B-cell lymphoma (DLBCL) in our hospital, based on next-generation sequencing mutation analysis and clinical characteristics. One or more mutations were detected in all patients, with a median mutation count of four (range 1\u0026ndash;9). The genes with the highest mutation frequencies (\u0026gt;\u0026thinsp;10%) included TP53 (37.3%), KMT2D (27.1%), CD79B (25.4%), PIM1 (22.0%), TNFAIP3 (18.6%), MYD88 (16.9%), IRF4 (15.3%), B2M (13.6%), TNFRSF14 (11.9%), CREBBP (10.2%), and SOCS1 (10.2%). Statistical analysis revealed that B symptoms, an International Prognostic Index score\u0026thinsp;\u0026gt;\u0026thinsp;2, and poor treatment efficacy were associated with inferior progression-free survival (PFS) and overall survival (OS). A mutation count greater than four, TP53 mutation, and KMT2D mutation combined with more than four mutations led to poorer OS and PFS, while IRF4 mutation was associated with better OS and a trend towards improved PFS. Therefore, it might be possible to identify high-risk patients in DLBCL through clinical characteristics and genetic mutation profiling, which may allow for personalized treatment leading to improved prognosis.\u003c/p\u003e","manuscriptTitle":"Prognostic Analysis of Diffuse Large B-Cell Lymphoma Patients Based on Clinical Characteristics, TP53 Mutation Status, and Number of Co-mutated Genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 02:28:33","doi":"10.21203/rs.3.rs-6665588/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cfe97a90-db10-4831-b200-dc10c514a8fc","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-11T07:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 02:28:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6665588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6665588","identity":"rs-6665588","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}